In [1]:
# load the data

import os
import glob 
from matplotlib.pyplot import rc_context
import seaborn as sns
import numpy as np
import pandas as pd
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
#sc.logging.print_versions()

import pickle  #upper threshold 값들을 저장하고 불러오기 위해서.
In [2]:
read_file = './data/results/scanpy_scanorama_corrected_ov_visium.h5ad'
adata = sc.read_h5ad(read_file)
In [3]:
import matplotlib as mpl
mpl.rcParams['figure.dpi']= 300
In [4]:
adata
Out[4]:
AnnData object with n_obs × n_vars = 32478 × 4313
    obs: 'in_tissue', 'array_row', 'array_col', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'n_counts', 'n_genes'
    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-10', 'gene_ids-11', 'gene_ids-12', 'gene_ids-13', 'gene_ids-14', 'gene_ids-15', 'gene_ids-16', 'gene_ids-17', 'gene_ids-18', 'gene_ids-2', 'gene_ids-3', 'gene_ids-4', 'gene_ids-5', 'gene_ids-6', 'gene_ids-7', 'gene_ids-8', 'gene_ids-9', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
    obsm: 'Scanorama', 'X_pca', 'X_tsne', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'
In [5]:
sc.pl.umap(adata, color="sample", title="Scanorama umap")
In [7]:
sc.tl.leiden(adata)

running Leiden clustering
    finished: found 24 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:06)
In [8]:
sc.pl.umap(adata, color=['leiden'], title="Leiden clusters umap")
In [25]:
sc.pl.umap(adata, color=['sample','leiden','PTPRC'], )
In [26]:
sc.pl.umap(adata, color=['sample','leiden','CD24'] )
In [24]:
sc.pl.umap(adata, color=['sample','leiden','EPCAM'] )
In [27]:
sc.pl.umap(adata, color=['sample','leiden','TFF1'] )
In [23]:
sc.pl.umap(adata, color=['sample','leiden','COL1A1'] )
In [22]:
sc.pl.umap(adata, color=['sample','leiden','COL1A2'] )
In [ ]:
sc.pl.umap(adata, color=['COL1A1'] )